Exosome in Crosstalk between Inflammation and Angiogenesis: A Potential Therapeutic Strategy for Stroke

The endothelial dysfunction, associated with inflammation and vascular permeability, remains the key event in the pathogenesis of cerebral ischemic stroke. Angiogenesis is essential for neuroprotection and neural repair following stroke. The neuroinflammatory reaction plays a vital role in stroke, and inhibition of inflammation contributes to establishing an appropriate external environment for angiogenesis. Exosomes are the heterogeneous population of extracellular vesicles which play critical roles in intercellular communication through transmitting various proteins and nucleic acids to nearby and distant recipient cells by body fluids and circulation. Recent reports have shown that exosomal therapy is a valuable and potential treatment strategy for stroke. In this review, we discussed the exosomes in complex interaction mechanisms of angiogenesis and inflammation following stroke as well as the challenges of exosomal studies such as secretion, uptake, modification, and application.

Introduction
Cerebral ischemic stroke is a common and serious cerebrovascular disease, accounting for 87% of all strokes [1], and is the leading cause of persistent disability. Although the incidence, prevalence, and mortality of stroke tend to decline, the results from the Global Burden of Stroke 2017 study indicated that the overall stroke burden keeps growing worldwide [2]. Ischemic strokes are caused by sudden cerebral vascular obstruction, and cerebral tissue ischemia and hypoxia can lead to mitochondrial dysfunction, decreased adenosine triphosphate (ATP), and excessive production of reactive oxygen species (ROS) after a few minutes [3]. This event results in the dysfunction of the sodium pump, calcium pump, and other ATP-dependent ion transporters, which causes Ca 2+ excretion obstacles and calcium overload [4]. Calcium overload promotes the release of glutamate, and glutamate increased in the intercellular space causes excessive excitation of glutamate receptors on the postsynaptic membrane, which further increases the loading of intracellular Ca 2+ and leads to cell necrosis [4]. Besides, after mitochondria were stimulated by calcium overload, the mitochondrial permeability transition pore (MPTP) was opened, and then, cytochrome C and other proapoptotic proteins are released through MPTP, which can lead to cell apoptosis [5]. In addition, when the blood supply to brain tissue is interrupted, ATP synthesis is decreased, and the increase of adenosine monophosphate (AMP)/ATP ratio can directly activate AMP-activated protein kinase and then inhibit the action of the mammalian target of rapamycin (mTOR), leading to cell autophagy [6]. Overall, these complex pathological cascades, such as calcium overload, glutamic acid excitatory toxicity [7], and oxidative stress, will eventually lead to edema, inflammatory reaction, necrosis, autophagy, apoptosis, and other serious consequences for the central nervous system cells [8], which will seriously affect the health and living quality of patients and increase the burden on families and society.
At present, the intravenous recombinant tissue plasminogen activator (IV-rtPA) was considered to be effective for acute stroke [9]; however, it is a pity that the therapeutic time window is only 4.5 hours. The risk might outweigh the benefit when the IV-rtPA was applied beyond 4.5 hours after stroke [10], and the hemorrhagic transformation and oxidative stress are major clinical risks, which will further amplify the pathological cascade [9,11]. Fortunately, the ischemic penumbra becomes the salvageable target for counteracting the expansion of the infarct core and promoting the recovery of stroke [12,13].
The interaction between inflammation and angiogenesis exists in many pathological processes, such as tumors, diabetic retinopathy, and stroke. Various proinflammatory cytokines, such as tumor necrosis factor α (TNF-α), interleukin 4 (IL-4), and monocyte chemoattractant protein 1(MCP-1) can promote the expression of vascular endothelial growth factor (VEGF) and the proliferation of endothelial cells and promote angiogenesis [14]. Similarly, the classical angiogenic factor VEGF also promotes the inflammatory response [15]. The crosstalk among different systems plays a crucial role in maintaining the homeostasis of the central nervous system and the integrity of the blood-brain barrier (BBB) both under physiological conditions and various diseases such as cerebral ischemia [16][17][18][19].
Exosomes, with a diameter of about 30-150 nm, are a heterogeneous population of extracellular vesicles equipped with functional molecule-decorated phospholipid bilayer and rich in proteins, lipids, and nucleic acids [20,21]. As an alternative therapeutic strategy to whole-cell implantation, exosomes play critical roles in intercellular communication through transmitting various proteins and nucleic acids to nearby and distant recipient cells by body fluids and circulation [22]. Recently, exosome-based treatment has shown immense effects in angiogenesis, anti-inflammation, neurogenesis, and antiapoptosis of stroke [23][24][25].
In this review, we summarize the biogenesis, uptake, and function of exosomes and the mechanisms of exosomes in crosstalk between angiogenesis and inflammation following stroke. And then, we discuss the challenges in the translation of exosome-based therapy to clinical applications.

The Features of Exosomes Applicable to the
Treatment of Stroke 2.1. The Advantages of Exosomes for Stroke. There are many treatments for stroke in the clinic, for instant, anticoagulants, blood pressure-lowering, and cholesterol-lowering drugs can prevent stroke; IV-rtPA and endovascular thrombectomy in the acute stage of stroke can timely achieve the blood flow recanalization [26]; some exercise training, physical factor therapies, and other rehabilitation treatments also can promote the restoration of nerve function in the stage of stroke convalescence [27]. However, numerous patients had poor control of the risk factors of stroke or can not arrive at the hospital within the time window of IV-rtPA and thrombectomy, resulting in severe neurological dysfunction. In summary, the available treatment strategies for stroke are limited; therefore, more research must be done on stroke. Neurorestorative therapies, as adjunctive therapy to improve stroke outcomes, mainly act on nerve cells, endo-thelial cells, and immune cells to promote neurovascular remodeling and reduce local and systemic inflammation [28]. Cell-based therapies are a neuro repair therapy for stroke by stimulating endogenous neuroplasticity and brain remodeling to promote neurological recovery following stroke [29]. Increasing evidence has indicated that the positive effects of cell-based therapies are mediated by exosomes, which are derived from the administered cells [22]. In addition, the exosomes have been shown that have many advantages [22], for instance, overcome the obstacles of BBB, can not block blood vessels, and can not induce malignant transformation. Therefore, exosomes provide a novel and potential therapeutic strategy for stroke.

Secretion and Regulation of Exosomes.
Almost all living cells can secrete exosomes in diverse pathophysiologic environments [30]. Several kinds of central nervous system cells such as microglia, oligodendrocytes, astrocytes, and neural stem cells (NSCs) can secrete exosomes and regulate the occurrence and development of neurological diseases. These central nervous system cell-derived exosomes play a key role in promoting angiogenesis, regulating inflammation, and remodeling the brain following stroke [25,31]. In addition, mesenchymal stem cells (MSCs) have attracted wide attention in stroke treatment because they can differentiate into neurons, but the MSCs were ≥15 μm in diameter [32], which makes it difficult to target them in the ischemic areas. Fortunately, exosomes have physiological functions similar to their donor cells; therefore, exosomes originated from central nervous system cells and MSCs are considered a potential therapeutic strategy.
Secretion and release of exosomes is a complex process, and multiple signal transduction factors, such as Rab GTPases, endosomal-sorting complex required for transport (ESCRT), and the ESCRT-associated protein ALIX are involved in the regulation of the secretion of exosomes [33][34][35]. The first step of the formation of exosomes is the cell membrane invagination which forms the early endosomes, and the further invagination of early endosomes wraps the proteins, lipids, and nucleic acids in the cytoplasm and form intraluminal vesicles (ILVs). The ESCRT family participates in the regulation of the molecular composition of the exosomes and the formation of ILVs, such as ESCRT-0, ESCRT-I, and ESCRT-II in charge of the cargoes sorting, and ESCRT-III undertakes the deformation and fission of membrane [36]. In addition, ALIX recruits ESCRT-III to endosomes and promotes sorting. Although the ALIX is an active form of the ESCRT-associated protein, the ALIX/ ESCRT-III pathway needs lysophosphatidic acid rather than being dependent on the ESCRT [36].
And then, with the gradual accumulation of ILVs, the early endosomes mature into late endosomes/MVEs, some MVEs fuse with the cell membrane and secrete exosomes through exocytosis, and the other MVEs are degraded by lysosomes [37][38][39]. The Rab GTPases are a critical family in this process. The conversion of Rab5 to Rab7 regulates the transition from early to late endosomes [36,40]. Rab27a and Rab27b are specialized in docking MVEs at the plasma membrane [41]. The Rab7 regulates the fusion of MVEs with 2 Mediators of Inflammation lysosomes to degrade the ILVs [36]. The Rab31 inhibits MVEs degradation by recruiting TBC1D2B to Rab7 and further suppressing the fusion of MVEs with lysosomes [42]. Currently, the initiation mechanisms of membrane invagination, the contents sorting (random or specific), and the balance between the degradation of MVEs by lysosomes and the formation of exosomes still needed further exploration.
2.3. The Uptake of Exosomes in the Central Nervous System. The recipient cells capture the exosomes in many ways, including specific molecular interactions, direct fusion of membrane, and various endocytosis involving macropinocytosis, lipid raft-mediated endocytosis, clathrin-mediated endocytosis, and clathrin-independent endocytosis [43][44][45][46]. Recipient cells uptake exosomes by specific molecular such as proteins, sugars, or lipids on the surface of the membrane [47]; for instance, the integrin lymphocyte functionassociated antigen 1 on the macrophage-derived exosomes can interact with intercellular adhesion molecule 1 (ICAM-1) on the brain microvascular endothelial cells (BMECs) [48]. The exosomal fusion directly with the membrane of the recipient cells accompanied several key events such as the rearrangement of exosomal membrane proteins, the hydrophobic sequences insert into the target cell membrane, lipid reorganization, protein reconstruction, and membrane dimpling [49]. Macropinocytosis internalizes large amounts of extracellular fluid by forming folds in the plasma membrane, which depends on the functions of actin, Rac1, and Na+/H+ exchanger [43]. The microglia uptake oligodendroglia-derived exosomes by macropinocytosis, and inhibiting the functions of actin, Rac1, and Na+/H+ exchanger resulted in a significant reduction of the uptake of exosomes by microglia [50]. In the process of BMECs uptake of macrophage-derived exosomes, Yuan et al. observed the phenomenon of clathrin-mediated endocytosis and caveolae-mediated endocytosis [48]. In addition, exosomes can also be taken in neurons such as dopaminergic neurons and affect these neurons. After the dopaminergic neurons ingest exosomes, the substantia nigra loss and apoptosis were reduced and the level of dopamine in the striatum was upregulated [51] and restored the homeostasis of oxidative stress, neuroinflammation, and cell apoptosis in the Parkinson's disease model mice [52]. Interestingly, exosomes secreted from stimulated by glutamatergic synapses cortical neurons and were specifically endocytosed by neurons [53]. The formation of exosomes, uptake by central nervous cells, and their effects are shown in Figure 1.
In general, several mechanisms of exosomal uptake have been identified, but the specificity of exosomes to recipient cells and the membrane molecules that participate in the recognition of exosomes by recipient cells still needed further investigation.

Exosomes in Inflammation following Stroke
3.1. Inflammatory Response following Stroke. Following cerebral ischemia, danger-associated molecular patterns, including high-mobility-group box 1 (HMGB1), peroxire-doxins, IL-33, mitochondrial transcription factor A, and cytochrome C, are released by damaged neurons in the minutes and bind to receptors on immune cells and then result in inflammation and neurotoxicity [54][55][56]. Activated microglia produce distinct phenotypes, M1 type secretes a plentiful of proinflammatory factors including IL-1β, IL-23, and TNF-α, which aggravate the inflammation [57]. Contrary to the M1 type, the M2 type plays an antiinflammation role in ischemic injury against oxygen and glucose deprivation [58]. Moreover, circulating immune cells including neutrophils, monocytes/macrophages, and T cells also aggravate central nervous system cell death due to the permeability of BBB increased after cerebral ischemia [54]. At the same time, cellular adhesion molecules on leukocytes and cerebral endothelial cells and cytokines were upregulated following stroke and then promote adhesion and leukocyte transendothelial metastasis, which further aggravates inflammatory response [54,59]. The astrocytes also produce two distinct phenotypes which, respectively, are A1 and A2 under different pathological conditions. Research has shown that the A1 type was induced by activated microglia under neuroinflammation, while the A2 type was induced by ischemia and plays a neuroprotective role via upregulating neurotrophic factors to promote neuronal survival and function repair [60,61]. But the remarkable thing is that neuroinflammation can contribute to the enlargement of infarcts; meanwhile, it can promote remodeling and repair at certain stages following stroke [54,62].

Exosomes in the Inflammatory Response following Stroke.
Exosomes participate in the remodeling process by delivering therapeutic nucleic acids, proteins, and drugs affecting inflammation following stroke. The MSC-derived exosomes packaged miR-542-3p target the Toll-like receptor 4 (TLR4) in HA1800 cells (human glial cells) and then prevent ischemia-induced glial cell inflammatory response [63]. Meanwhile, in the inflammation following stroke-induced reactive astrogliosis, the MSC-derived exosomes target astrocytes and reduce the reactive astrogliosis and inflammation; the undergoing mechanism might be related to nuclear factor erythroid-derived 2 (Nrf2)/nuclear factor-κB (NF-κB) [64]. Bone MSC-(BMSC-) derived exosomes with miR-138-5p target the lipocalin 2 (LCN2) on astrocytes and reduce the neurological impairment by inhibiting the astrocytes' inflammatory response and apoptosis after stroke [65]. Melatonin is an effective free radical scavenger and antioxidant, and melatonin-treated plasma exosomes significantly protect against ischemia-induced inflammatory response and inflammasome-mediated pyroptosis via regulating the TLR4/NF-κB pathway [66]. The neural progenitor cell-(NPC-) derived EVs showed the strong suppression of the inflammatory response following cerebral ischemia, and the effect is related to a set of miRNAs packaged in the EVs that inhibited the inflammation-related pathway mitogenactivated protein kinase (MAPK) [67].

Function of Exosomal miRNAs in Inflammation
Response following Stroke. Except for exosomal miRNAs, there are many proteins and DNA also involved in some 3 Mediators of Inflammation crucial biological processes. For instance, tetraspanin (CD9, CD63, and CD81) and integrin proteins are essential for cell targeting and adhesion, Rab GTPases, annexins, and flotillin contribute to membrane fusion, and G proteins and kinases are signal transduction molecules [20,68]. Furthermore, in a few types of research, genomic and mitochondrial DNA has been found in exosomes [69]. However, there are few studies on exosomal proteins and DNA in the field of neuroinflammation and angiogenesis following stroke, and increasing evidence has suggested that the exosomal miRNAs are largely responsible for the therapeutic effects [70,71].
Recent research suggests that exosomal miRNAs have dual effects [71]. On the one hand, the exosomal miRNAs negatively regulate the expression of target genes; for instance, M2 microglia-derived exosomes attenuate ischemic brain injury and promote neuronal survival via exosomal miR-124 which negatively regulates its target gene ubiquitin-specific protease 14 [72]. On the other hand, the exosomal miRNAs as a ligand binds to the receptor; for instance, the exosomal miR-21 and miR-29a have demonstrated that bind to TLR and induce TLR8-mediated activation of NF-κB and NF-κB-mediated secretion of proinflammatory cytokines TNF-α and IL-6 [73].
In addition to exogenous miRNAs, exosomal miRNAs are also critical in regulating inflammation following stroke. Microglia are prominent resident immune cells in the central nervous system, and the activated microglia trigger severe inflammatory responses following stroke. The MSCderived exosomal miR-223-3p inhibits M1 microglia polarization and inflammatory response [74]. Moreover, the human umbilical cord MSC-(hUMSC-) derived exosomes also attenuate microglia-mediated neuroinflammation and promote the recovery of neural function, and the undergoing mechanism is involved in that exosomal miR-146a-5p regulates the IL-1 receptor-associated kinases 1 (IRAK1)/TNF receptor-associated factor 6 (TRAF6) [75]. Serum exosomes from acute cerebral infarction patients aggravate cerebral inflammation and promote microglia activation in middle cerebral artery occlusion (MCAO) rats, and the undergoing mechanism might be related to the exosomal miR-27-3p which targets the peroxisome proliferator-activated receptors γ (PPARγ) [76]. The PPARγ is a ligand-activated transcriptional factor that participates in regulating a variety of signaling networks including inflammation, glucose homeostasis, and cell fates [77]. BMSC-derived exosomal miR-221-3p can target the activated transcription factor 3 (ATF3) and then attenuate the neuroinflammation and apoptosis following stroke [78].
While plentiful studies have proved that exosomes can regulate inflammation after stroke, current studies have mainly focused on the miRNAs of exosomes. The effect of other exosomal cargoes on inflammation after stroke needs further study.

Exosomes in Crosstalk between
Inflammation and Angiogenesis following Stroke 4.1. Crosstalk between Inflammation and Angiogenesis following Stroke. Angiogenesis usually occurs in the inflammatory environment, the inflammatory response is usually accompanied by the initiation, progression, and stability of angiogenesis [79]. Increasing research has indicated that many immune cells and cytokines not only secrete inflammatory factors to aggravate brain injury but also promote  Figure 1: Summary of the exosomal secretion, uptake, and the effect of regulating inflammation and angiogenesis. Process A represents the exosomes derived from donor cells such as ADSCs, MSCs, and EPCs. This complex process is relevant to the formation of early endosomes, early endosomes mature into late endosomes, and MVEs fuse with the cell membrane and secrete exosomes. Process B is exosomes that bind to the central nervous system cells such as microglia, astrocytes, and endothelial cells through specific molecular interactions, the direct fusion of membrane, and various endocytosis. Process C shows the effect of exosomes in regulating inflammation and angiogenesis by promoting the formation of the blood vessel and suppressing the production of neuroinflammatory mediators following stroke. Abbreviations: ADSCs: adipose-derived stem cells; MSCs: mesenchymal stem cells; EPCs: endothelial progenitor cells; MVEs: multivesicular endosomes; ESCRT: endosomal-sorting complex required for transport. 4 Mediators of Inflammation angiogenesis and BBB repair in a later stage [80,81]. It is well-known that the M2 type microglia affect angiogenesis and anti-inflammation through secreting the IL-10, transforming growth factor β (TGF-β), insulin-like growth factor (IGF), and VEGF [57]. In the early phase of stroke, the monocytes destroy BBB integrity and exacerbate neuroinflammation through secreting ROS, cytokines, and chemokines [80]. However, the function and phenotype of monocytes are not unchangeable; following differentiation into mature macrophage cells, the monocyte-derived M2 type macrophage cells have a stronger ability in angiogenesis by upregulating the basic fibroblast growth factor (FGF), IGF-1, and placental growth factor (PGF) [80,82]. The pericytes were considered immune cells in the brain, and the pericytes' membrane protein TLR4 can bind the HMGB1 and induce the secretion of proinflammatory cytokines and chemokines shortly after stroke. Moreover, pericytes are essential for the whole process of angiogenesis including degrading the basement membrane surrounding endothelial cells, promoting the formation of tip cells by endothelial cells, and preventing the degradation of matrix proteins of neovascularization by secreting matrix metalloproteinase, VEGF, and tissue inhibitor of metalloproteinase-3 [83][84][85].
In addition, the proinflammatory cytokines such as TNF-α, MCP-1, and SDF-1 also can affect angiogenesis. The research has shown that the interaction of TNF-α with TNF receptor 1 can amplify the effect of erythropoietininduced angiogenesis by upregulation of the erythropoietin receptor [86]. Significantly, a high dose of TNF inhibits angiogenesis, but the pericytes can enhance angiogenesis and overcome the inhibition of angiogenesis induced by a high dose of TNF [79]. The MCP-1 is a key regulatory molecule of monocytes trafficking to sites of inflammation; meanwhile, the MCP-1 is a chemokine with angiogenic properties. The angiogenic effect of MCP-1 is maintained and modulated by VEGF [87]. The stromal cell-derived factor-1 (SDF-1), also called C-X-C motif chemokine ligand 12 (CXCL12) binds to the CXC receptor 4 (CXCR4) as an inflammatory initiator in the acute phase of stroke [88]. During the postacute phase of stroke, the CXCL12 promotes angiogenesis by recruiting circulating endothelial progenitor cells (EPCs) [89]. In addition, SDF-1 promotes neurogenesis and angiogenesis through CXCR4-mediated downstream protein kinase B (AKT), extracellular signal-regulated kinases (ERK), and P38 MAPK signaling pathways [88,90].
Similarly, angiogenic factors and growth factors can induce inflammatory responses [81]. Endothelial cellspecific molecule 1 (Esm1) has dual effects on angiogenesis and inflammation, and the research has shown that knockout Esm1 can reduce vascular permeability and cerebral edema following stroke [91]. Moreover, gathered VEGF directly participates in the proliferation, migration, and differentiation of endothelial cells [92,93], but VEGF is also a chemokine that causes an increase in vascular permeability and inflammation. Clinical evidence has shown that circulating VEGF was elevated 24-48 h after acute stroke and conveyed severe prognostic information [94]. HIF-1 is a kind of nuclear protein with transcriptional activity induced by hypoxia [95]. When mild and moderate ischemic hypoxia occurs, HIF-1 is upregulated and then promotes angiogenesis in the ischemic region by directly upregulating the expression of VEGF [96][97][98][99]. However, HIF-1 also has an opposite effect that is bad for functional repair following stroke. Several experiments have shown that upregulated HIF-1 could aggravate apoptosis, autophagy, oxidative stress, and inflammation [98,100]. Although brain-derived neurotrophic factor (BDNF) is a neurotrophic protein that is widely expressed in the central nervous system and it can promote angiogenesis and neurogenesis [101], increased BDNF can promote the inflammatory response by increasing neutrophil infiltration [102]. Besides, the notch signaling pathway directly receives signals from neighboring cells, the signals are transmitted to the nuclear as well as activate the expression of related transcription factors and then regulate angiogenesis [103]. Likewise, notch signaling can also regulate inflammation in activated microglia in cerebral ischemia [104,105].
In summary, the crosstalk between inflammation and angiogenesis following stroke relates to the interaction of various cells, cytokines, and signaling pathways in the central nervous system.

The Effects of Exosomes in Crosstalk between
Inflammation and Angiogenesis following Stroke. The circulatory system and immune systems have complex interactions, so some new therapeutic measures are needed to address the challenges of inflammation and angiogenesis in distinct environments. Exosomes can be used as a potential tool, which reprograms recipient cells by providing numerous factors of proangiogenesis and regulating inflammation such as nuclear acids, noncoding RNAs, and proteins. The effects of exosomes on crosstalk between inflammation and angiogenesis are shown in Figure 2.

MSC-Derived
Exosomes. The transplanted MSCs increase the vascular density in infarcted areas via upregulating the expression of angiogenic factors such as HIF-1, VEGF, and Angiogenin 1 (Ang-1). The improvement effects not only include the promotion of the maturation and stability of blood vessels, the activation of endothelial cells, vascular smooth muscle cells, and pericytes but also include the reduction of inflammation and vascular leakage [106][107][108][109][110]. Moreover, MSCs can attenuate the circulating immune cells including B-cells, natural killer cells, and T-cells, which provide an appropriate external environment for brain remodeling [111]. Similarly, MSC-derived exosomes were certificated that participate in angiogenesis and inflammation. For example, MSC-derived exosomes with miR-29b-3p promote angiogenesis via targeting phosphatase and tensin homologue deleted on chromosome 10 (PTEN)/ AKT pathway [112], and the PTEN/AKT indicated an increased ratio of M2 polarization to M1 polarization and thus inhibiting inflammation [113]. MSC-derived and loaded cholesterol-modified miR-210 exosomes target the ischemic brain and promote angiogenesis by upregulating the integrin β3, VEGF, and CD34 [114]. The MSC-derived exosomes decorated with iron oxide nanoparticles (IONP) exhibit a significant therapeutic effect on ischemic stroke, 5 Mediators of Inflammation this effect is connected with angiogenesis, anti-inflammation, and antiapoptosis [23]. Notably, the IONP-decorated exosomes not merely obtain a better targeting ability but also activate the phosphorylation of c-Jun N-terminal kinase (JNK) and upregulate the expressions of angiogenic factors (Ang-1, FGF2, and VEGF) and anti-inflammation factors (TGF-β1 and TGF-β3) [23]. Ischemic stroke may cause more severe dysfunction in aged humans; in an animal research, MSC-derived EVs reduced brain macrophage infiltrates in aged MCAO rats and the accumulation of microglia in young rats. Meanwhile, the EVs increased angiogenesis in young and aged rats [115]. A hypoxic environment can stimulate angiogenesis; similarly, hypoxic-treated MSC-derived EVs were indicated that effectively increase BMEC proliferation, migration, and tube formation and promote the postischemic survival of BMECs [116]. In addition, MSC-derived exosomes significantly increase the number of newly formed doublecortin (a marker of neuroblasts) and von Willebrand factor (a marker of endothelial cells) and enhance neurovascular remodeling following stroke [117].

EPC-Derived Exosomes.
The EPCs not only merely migrate to the injury zone, participate in the angiogenesis, and restore the integrity of BBB but also have an important role in regulating the inflammatory response, lessening the motor and neurological impairments associated with stroke pathology [19,118]. EPCs promote endothelial regeneration by stimulating the proliferation and migration of endogenic endothelial cells via paracrine mechanisms, instead of direct differentiation into mature endothelial cells [119,120]. As a key component of paracrine secretion, the EPC-derived exosomes have been confirmed to promote angiogenesis and improve the recovery of function [121]. In recent studies, Wang et al. observed that EPC-derived exosomes decrease infarct size and increase cerebral blood flow and cerebral microvascular density, especially the exosomes with enrichment miR-126 enhance the therapeutic efficacy [122], and miR-126 has been found that promote angiogenesis and inhibit inflammation by repressing sprouty-related EVH1 domain-containing protein 1 (SPRED1) and vascular cell adhesion molecule 1 (VCAM1) [123]. In addition, EPC-  Figure 2: Summary of the exosomes in crosstalk between inflammation and angiogenesis. The red arrow represents regulation of inflammatory response, the yellow arrow represents regulation of angiogenesis, and the blue arrow represents general regulation. Exosomes from different sources carry miRNAs and other contents that affect central nervous system cells and simultaneously regulate inflammation and angiogenesis through different signals. The balance between inflammation and angiogenesis is also influenced by many factors following stroke, including the pathological stage of stroke, the signal received, and the dose of therapeutic substances. Mediators of Inflammation  [129] 7 Mediators of Inflammation derived exosomes enriched with miR-137 could be against apoptosis and mitochondrial dysfunction in SH-SY5Y cells, and the protective efficacy might be connected with the cyclooxygenase 2 (COX2)/prostaglandin E2 (PGE2) signaling pathway [124], COX2 is expressed in inflammation and other pathological processes, and the beneficial effect of anti-inflammation is connected with inhibition of COX2.

Adipose-Derived Stem
Cell-(ADSC-) Derived Exosomes. The ADSCs are self-renewing pluripotent stem cells derived from adipose tissue, and ADSCs have the characteristics of easy selection, nonimmunogenicity, and low risk in teratogenesis and tumorigenesis. ADSCs also play an important role in angiogenesis and immune regulation [125]. It has been demonstrated that ADSC-derived exosomes with miR-181b-5p promoted the mobility and angiogenesis of BMECs by targeting the transient receptor potential cation channel member 7 (TRPM7) following oxygen-glucose deprivation [126], and TRPM7 is a major controller that produces proinflammatory cytokine and activates NF-κB by transmitting Ca 2+ ; notably, angiogenesis and vascular remodeling also depend on the Ca 2+ signal [126][127][128]. Moreover, the ADSC-derived exosomes enriched miR-126 against cerebral ischemia injury by promoting angiogenesis after stroke; meanwhile, the exosomes inhibit microglial activation and inflammatory response induced by stroke [129].
To sum up, exosomes are a cell-based therapy and the biological functions of exosomes are closely related to the biogenesis, cargoes, and target cells. Current researches focus on the stem cell-derived exosome, whereas there are numerous immune cells such as microglia-derived exosomes for crosstalk between inflammation and angiogenesis following stroke which needs further investigation. The roles of exosomes in inflammation and angiogenesis following stroke are shown in Table 1.

Conclusions and Perspectives
Given the above, inflammatory cells and cytokines may promote angiogenesis at different stages after stroke, and angiogenic factors also may induce inflammation depending on many complicating factors such as the dose, signal received, stage of stroke, and microenvironment. The interaction between neuroinflammation and angiogenesis dictates the injury and repair processes following stroke. It is crucial to develop therapeutic measures that target multiple systems to maximize therapeutic efficacy. Numerous evidences have indicated that exosomes exert protective and restorative through regulating the interaction of angiogenesis and inflammation effects in stroke.
Although exosomes have shown great therapeutic potential in the treatment of stroke, many challenges need to be clarified before exosomes are used as a new method for stroke. ① How to prepare more exosomes? The secretion of exosomes is finite, which limits the clinical application. Therefore, scale production is essential. At present, the measures to obtain a large number of exosomes mainly include the addition of elicitors or drugs, change the culture conditions, and extrusion. However, these methods may change the exosomal contents and affect the therapeutic effects. ② How to increase the targeting of exosomes? The drawback of undecorated exosomes is the poor targeting, thereby yielding a poor therapeutic outcome. Engineering exosomes have better targeting; for example, the MSC-derived exosomes modified with IONP can drastically improve the ischemiclesion targeting [23,25]. But the preparation of engineered exosomes involves multiple reaction steps that may affect the exosomal surface structures and molecules, resulting in adverse reactions. ③ How do loading the required drugs and bioactive substances into exosomes? The existing methods have coincubation of parental cells with drugs, directly loaded into exosomes, and directly incubated with exosomes. Using the method of coincubation of parental cells with drugs to secrete engineered exosomes is simple to operate but can not control the efficiency of drug delivery. It is a widely used method that the drugs are loaded directly into exosomes such as electroporation. The process is the drug diffuse into exosomes following the formation of the temporary pores on the exosomal membrane by electric stimulation. However, the process may damage the membrane structure of exosomes and lead to drug leakage. In addition, the drugs directly incubated with exosomes can not affect the integrity of membrane structure, and the loading efficiency is very limited and requires a large dose of drugs.
As described above, the challenges, activity, and therapeutic effects of exosomes can be affected easily. Therefore, comprehensive consideration of the secretion, preparation and uptake of exosomes, in-depth understanding of molecular transfer mechanisms and biological characteristics, and selection of appropriate modification and loading methods are of irreplaceable significance to better play the role of exosomes.

Conflicts of Interest
The authors declare that they have no conflicts of interest.


Analysis of the Holarctic Dictyoptera aurora Complex (Coleoptera, Lycidae) Reveals Hidden Diversity and Geographic Structure in Müllerian Mimicry Ring

Simple Summary We evaluated the red net-winged beetle populations’ morphological and genetic divergence within the Holarctic region. In contrast with relatives, D. aurora occurs in an exceptionally large range and very different ecosystems. In Northern America, we found an earlier undetected cryptic species isolated by the Bering Strait since the mid-Miocene. D. aurora colonized Fennoscandia from at least two refugia after the last glacial maximum. The absence of morphological differentiation is supposedly affected by the selection for similarity in the Müllerian mimicry ring. The results exemplify the phylogeographic history in the Holarctic region and contribute to understanding the morphological stasis in an extensive circumpolar range. Abstract The elateroid family Lycidae is known for limited dispersal propensity and high species-level endemism. The red net-winged beetle, Dictyoptera aurora (Herbst, 1874), differs from all relatives by the range comprising almost the entire Holarctic region. Based on a five-marker phylogeny and 67 barcode entries (cox1-5′ mtDNA) from the whole range, we recovered two genetically distinct species within traditionally defined D. aurora and resurrected the name D. coccinata (Say, 1835) as the oldest available synonym for Nearctic populations. Yet, no reliable morphological trait distinguishes these species except for minute differences in the male genitalia. D. coccinata is a monophylum resulting from a single Miocene dispersal event, ~15.8 million years ago, and genetic divergence implies long-term isolation by the Bering Strait. Far East Asian and west European populations are also genetically distinct, although to a lower extent. Two independent colonization events established the Fennoscandian populations after the last glacial maximum. Besides intrinsic factors, the high morphological similarity might result from stabilizing selection for shared aposematic signals. The rapidly accumulating barcode data provide valuable information on the evolutionary history and the origins of regional faunas.

Introduction
Molecular data have become essential sources of information in biodiversity studies, and they are especially valuable if diagnostic morphological traits are contentious [1][2][3][4][5]. Then, DNA data can solve the delimitation of cryptic species [2,6,7]. Mitochondrial markers are also widely used for the rapid genetic screening of biodiversity, and the method can be increasingly used with next-generation sequencing [8][9][10][11]. Although the mtDNA resolution to detect subtle intraspecific differentiation is low [7,[12][13][14], even short fragments are sufficient for obtaining the first clue to shallow relationships and identification of discrepancies between traditional morphology-based taxonomy and genetic diversity [2,15,16].
Here, we focus on an exceptionally widespread red net-winged beetle, Dictyoptera aurora (Herbst, 1784) (Erotinae: Dictyopterini) that belongs to the predominantly tropical gene was performed with ModelFinder [45,46] implemented in IQ-TREE2 (-MFP option). The GTR model was applied in all analyses. Ultrafast bootstrap [47] values were calculated in IQ-TREE (options -bb 5000) to assess nodal supports for focal relationships.
Further, we generated a haplotype network based on the cox1-5 fragment using the TCS algorithm [48] in PopART [49]. The output was processed in Adobe Illustrator.
The dated tree of the genus Dictyoptera Latreille was taken from the whole family analysis (the dataset used by [50] and expanded by unpublished data). Beast v.1.8 [51] was used to date splits among the Lycidae family with calibrated separation of Lycidae + Iberobaenia to 176 my. The following settings were employed: the unlinked partitions (genes and codon positions were partitioned in the (1 + 2),3 scheme, the HKY + I+G model, and the birth-death speciation prior. The run was computed for 5 × 10 7 generations with a sampling frequency of 10,000. The ESS values and plateau phases were checked using TRACER v.1.681 [52]). The maximum credibility tree was generated with TREEANNOTA-TOR v.1.8.1 [51].

Morphological Study
About 100 individuals deposited in the senior author's laboratory from the Nearctic and Palearctic regions were investigated to identify putative diagnostic characters and intraspecific variability. We specifically compared the shapes of the antennae, pronotum, and elytral costae. Further, we dissected multiple individuals from the whole range to study the intraspecific variability in the male genitalia. The voucher specimens were relaxed in 50% ethyl alcohol for an hour and then detached abdomens were treated with a hot 10% KOH solution to remove muscles and body fat. The external characters and genital morphology were observed under an Olympus SZX-16 microscope and photographed by an attached digital camera. We used Helicon Focus (www.heliconsoft.com, accessed on 21 January 2022) and Photoshop 13.0 (Adobe Inc., San Jose, Ca, USA) to assemble photo stacks in figures showing the principal morphological traits.
The individuals used for morphological investigation and sequenced specimens bearing the UPOL voucher number are deposited in the collection of the Laboratory of Biodiversity and Molecular Evolution, CATRIN-CRH, Olomouc.

Phylogeny, Genetic Diversity and Distribution
In our five-gene phylogenetic analysis, we find two distant clades represented by the individuals traditionally assigned to the morphospecies D. aurora ( Figure 1). The North American populations are represented by a single individual from the Eastern USA, and the Euro-Asian populations by three individuals from the Russian Far East and four from Central Europe ( Figure 1C). The dating analysis using the cox1-3 , rrnL, nad5 mtDNA dataset suggests that Nearctic and European populations separated already in the mid-Miocene, 15.75 million years ago ( Figure 1D). Their generic divergence is documented by uncorrected pairwise distances in mitochondrial and nuclear markers (complete SSU RNA 0.3%, D2 loop of LSU rRNA 0.3%, rrnL mtDNA 4.3%, cox1-3 13.8%, nad5 9.6%). We suggest the presence of D. aurora in the Western Palearctic shortly before the end of the Miocene, 6.23 mya when the Far East and European populations split ( Figure 1D).
Further, the cox1-5 mtDNA (barcode) fragments were analyzed from the whole range. The uncorrected pairwise distances of the barcode cox1-5 fragments reached 12.5% (Table S1). The phylogenetic network based on the cox1-5 fragment confirms separate clusters of haplotypes corresponding to the main branches recovered by the phylogenetic analysis ( Figure 1B,C,E). Further, we identified European populations' relatively high intraspecific genetic diversities. The genetic divergence between Fennoscandian populations reaches 7.2% in cox1-5 (barcode) fragment. Additionally, we constructed the barcode networks for three lycid species occurring in Central Europe and Fennoscandia ( Figure 1F-H). L. sanguineus and Pl. minuta comprise genetically close populations ( Figure 1F,G). P. nigroruber contains genetically divergent populations in Fennoscandia ( Figure 1H).

Differential Diagnosis
Both D. coccinata and D. aurora are morphologically highly variable species, and we have not found any external traits for identifying these sister species (Figures 2 and 3). We only noted minor, potentially unreliable, differences in the shape of the phallus. The basal process (pointing ventrally to the phallobase) is slenderer in D. coccinata. Still, there are intraspecific differences, and although the trait is very apparent in some individuals ( Figure 3M), it is hardly observable in others ( Figure 3O). Additionally, the ventral edge of the D. coccinata phallus is concave to straight in the basal half, unlike the simply concave edge in the whole length of D. aurora (observed in the ventrolateral view; Figure 3G-Q).

Intraspecific Variability
High intraspecific variability of external characters includes the shape of the male antennae, pronotum, and the arrangement of elytral costae (Figure 2A-J). The males have

Intraspecific Variability
High intraspecific variability of external characters includes the shape of the male antennae, pronotum, and the arrangement of elytral costae (Figure 2A-J). The males have slenderer antennae than females, but we noted differences in the relative length of antennomeres also in males ( Figure 3A-E). Similarly, we observed differences in the shape of the pronotum ( Figure 2E-J). Unlike fully sclerotized beetles, the asymmetry of the pronotum is commonly encountered, and defective structures are common in soft-bodied lycids ( Figure 2F). The net-winged beetles are so weakly sclerotized that dry-mounted individuals have regularly deformed elytra. Then, it is difficult to compare the strength of elytral costae. Therefore, we photographed the specimens immediately after mounting to show the differences (Figure 2A-D). The specimens in Figure 2A,C,D have slightly stronger primary costa 2 that reaches the apex of elytra; the specimen in Figure 2B has merged primary costae 2 and 3. The primary costa 2 is not only shorter but also relatively weaker. Similarly, the male genitalia is variable. There are differences in the basal part of parameres (pointed in Figure 3L,M and straight in Figure 3P,Q), the conspicuousness of thorns at the inner margin of parameres (acute in Figure 3L,M,G, obtuse in Figure

Biology
D. coccinata is a locally common forest species in mid to high-latitude mountains; larva is unknown. Adults are active from March in the south until late June in the hi mountains and the northern part of its range.

Biology
D. coccinata is a locally common forest species in mid to high-latitude mountains; its larva is unknown. Adults are active from March in the south until late June in the high mountains and the northern part of its range.

Distribution
Nearctic Region: USA (from northern Florida and Eastern Texas to the Canadian border; Arizona, New Mexico, eastern slopes of the Rockies, southwestern South Dakota, Alaska) and Canada to the northern limit of spruce forests. Most records are available from the Rockies and Appalachian Mountains [19]. Further information was taken from www.inaturalist.org, accessed on 2 July 2022. The records close to range limits were revised, and misidentifications were excluded). The northernmost record is known from central Alaska (Chena River, 65.9 • N).

Note
The mature larva of D. aurora was reported by Kazantsev and Nikitsky [31]. Here, we describe the second instar for comparison.

Biology
The larvae live in rotten, red-colored wood that remains wet for most of the year; typically in large stumps and trunks. Larvae were found in tree roots up to 20 cm deep in the soil. The species is locally common in mountain forests but occurs in lowlands in high-latitude regions. Larvae pupate in autumn, and adults overwinter under the bark or in wood crevices, usually in aggregations, which sometimes contain up to several dozens of individuals. They leave the wood in the early spring. Adults remain primarily inactive, either sitting on rotten wood or herb leaves under the forest canopy, close to the place where they developed. The flight of adults is slow, restricted to shaded situations and favorable weather conditions (high humidity, no wind). The adults occur in nature for about three weeks, from late April until June, depending on the local climate. The males die shortly after copulation, and the females live for another week. The females lay eggs in rotten wood, usually in shallow holes. The first instar larvae hatch after two weeks and immediately actively search for shelter deep in rotten wood. The larvae do not build tunnels and use crevices to move within the substrate. Larvae were growing rapidly in moist wood, but we did not manage to observe the whole life cycle, and the length of development remains unknown.

Taxonomic Decision
There are seven available species-group names in the D. aurora complex [56][57][58]60,61,63,64]. Kleine [37] broadened the concept of D. aurora and considered other names as junior subjective synonyms. Phylogenetic analyses show that this complex consists of at least two genetically distinct clades geographically separated by the Bering Strait. We consider the Nearctic clade a separate species (see Results: phylogeny). Several available synonyms are listed for D. aurora, and the oldest available name referring to Nearctic populations is Omalisus coccinatus Say, 1835. Therefore, we designate the Nearctic species as Dictyoptera coccinata (Say, 1835). Although we found genetic differentiation between Russian Far East and European populations, we refrain from the resurrection of D. superba (Motschulsky) due to the lack of high-resolution nuclear markers and information on the eventual sympatric occurrence. Similarly, the cox1-5 genetic difference between sympatrically-occurring individuals of D. aurora from Fennoscandia (Table S1) surpasses the widely accepted 2-3% threshold for the species delimitation [1], but see [66] for the genetic differentiation of geographically distant populations). The taxonomic status of European populations can be reconsidered when D. aurora is densely sampled in the whole continent.
The holotype of O. coccinatus is unavailable as Say's collection was destroyed [67]. The name has been listed as a junior subjective synonym of D. aurora since Kleine [37], but due to an error, it is listed as a synonym of Lygistopterus sanguineus in the GBIF database (https: //www.gbif.org/species/161597475; accessed on 1 June 2022). L. sanguineus does not occur in the Nearctic region, and the original description clearly states that the pronotum bears the median areola [56]. We refrain from a designation of the neotype as the present study is not a taxonomic revision, and we consider the identity of the species uncontentious [68].

Morphological and Genetic Differentiation
Morphology is the traditional, cheap, and rapid source of diagnostic traits, but morphology-based identification has limitations and warrants validation by independent molecular data [2,15,65]. Our study of the very widespread morphospecies D. aurora reveals yet unknown genetic diversity (Figure 1) and the absence of undisputable diagnostic traits that would discriminate the genetically distant populations as candidate species (Figures 2 and 3).
The intraspecific morphological variability surpasses identified differences between both species (Figures 2 and 3). We found only very subtle differences between Nearctic and Palearctic specimens in the shape of the basal part and the ventral edge of the phallus ( Figure 3F-Q). The students of net-winged beetles have never mentioned these. Indeed, they would not be considered sufficient to separate the Nearctic and Palearctic populations as distinct biological species if we do not have molecular data indicating genetic differentiation, reciprocal monophyly, and long-term geographical isolation. In contrast with these, male antennae ( Figure 3A-D), the pronotum ( Figure 2E-J), the relative strength and length of longitudinal elytral costae (Figure 2A-D), and the general shape of the male genitalia ( Figure 3F-Q) are quite variable within populations. D. aurora's genetic divergence supports a hypothesized causality between low vagility and high diversification rate [7,12,69]. Yet, the placement of all populations in a single morphospecies challenges the intuitively expected link between genetic and morphological diversification.
The levels of mtDNA divergence detected in the D. aurora clade are comparable to those often found between morphologically species pairs in other insects [1,15,66] and also between the sister species of net-winged beetles [12,27]. The genetic diversification between Nearctic and Palearctic populations of the D. aurora complex is higher than between many species' pairs identified in previous studies. The split between the Nearctic and Palearctic populations was estimated to be 15.75 mya ( Figure 2B). Therefore, we split the D. aurora complex into two (semi)cryptic species with~16 million years of independent evolution.
The genetic divergence exceeds thresholds used for algorithmic identification based on barcode data. Eleven bins (clusters) are proposed in the BOLD database for the D. aurora complex based on 67 barcodes (www.boldsystem.org, accessed on 1 June 2022). It is an anomaly as most cox1-3 mtDNA clusters approximately correspond with biological species, or the species are split into only a few genetically distinct groups if data originate from disjunctive areas [2,66].
Although we deal with a single morphospecies and morphological stasis is a macroevolutionary and paleontological concept [70,71], it is worth discussing the processes possibly leading to such an undisputable lack of morphological divergence (Figures 2 and 3). All net-winged beetles share very similar biology, yet most closely related species are morphologically distinct [27,32,72]. For example, their distinctiveness is often a result of advergence to various Müllerian co-mimics [28,50,[73][74][75]. Further diagnostic traits are regularly found in the male genitalia that plays a role in sympatric speciation [76,77]. Therefore, intrinsic constraints undoubtedly play a role in morphological stasis, but ecological factors also affect the divergence of the phenotype.
The origin of the D. aurora complex and its striking morphological uniformity stands in contrast with a new Ferreira's hypothesis that stable paleoenvironment is a driver for conserved morphology in a paedomorphic lineage [44]. The hypothesis is based on a single, 15 million years (my) old, Dominican amber neotenic species, its extant relative placed in the same genus, and the stable position of Hispaniola in the tropics. All populations of the D. aurora complex are morphologically uniform (Figures 2 and 3), yet the morphological stasis due to stable environmental conditions is exceptionally improbable as D. aurora occurs from Northern African cedar forests to tundra low shrub ecosystems beyond the polar circle in Fennoscandia and D. coccinata from dry shrub and forest vegetation in Arizona to the polar circle area in Alaska and the humid temperate to subtropical forests of Georgia and northern Florida. Following the argumentation presented by Ferreira et al. [44], we would have to analogically propose that the unstable and diverse environments select for the morphological stasis of the D. aurora complex.
The linkage of morphological stasis and neoteny is another aspect of Ferreira's theory that is contradicted by the conserved morphology of fully metamorphosed D. aurora (Figures 2 and 3). First of all, the neotenic net-winged beetles are morphologically disparate. They represent 2% of net-winged beetle species diversity but account for 25% of the described morphology-based supergeneric taxa (12 of 48 proposed; [18,78,79]. We might also consider that the ontogenetic modifications have a substantial impact on the phenotypes, and neoteny, or more broadly pedogenesis, are important macroevolutionary drivers producing morphological disparity [80]. The net diversification rate of neotenics must be considered, i.e., speciation and extinction [81]. The high extinction rate is possibly the reason why a low net diversification was observed in neotenics after the colonization of new landmasses and an initial short-term diversification burst [82,83]. Another question is the time factor. The morphologically uniform D. aurora complex split from its closest, phenotypically similar relatives at least 30 my before the time of Dominican amber (54 versus 23 my or 15 my) [44,84]. An ancient origin of the Dictyoptera-like phenotype is also supported by Priabonian Erotinae [85][86][87]. The earliest split between the cryptic species, D. aurora and D. coccinata, is estimated at 15.75 mya (7.18-26.06 mya 95% confidence interval; Figure 1D). The Dominican leptolycine species underlying Ferreira's hypothesis is slightly younger than the earliest split within the morphospecies earlier designated as D. aurora. The age of the Dominican fossil species disqualifies it from a long-term stasis evaluation. The dated phylogenies of net-winged beetles have shown that many genera with morphologically uniform species can be traced to the Paleogene [13,28,50]. As the studies were published only recently, Ferreira and collaborators [44] possibly missed them. Still, the authors did not compare the time since the establishment of the conserved morphology of their Dominican net-winged beetle with other beetles known for morphological stasis. Examples include morphological similarity of extant and early Mesozoic elateroids; mid-Cretaceous and Priabonian lycids [88][89][90][91][92][93][94] but not [95] who reported a Cretaceous tenebrionoid beetle as a lycid by error; extant and mid-Cretaceous lymexylids [96,97], jacobsonids [98], or micromalthids [99]. Growing information on Kachin amber provides further examples in various lineages [100].
The ancient morphological stasis was also recovered by dated phylogenies [101,102]. To sum up, there is plenty of evidence that morphological similarity of closely related species (within genera, usually with the earliest common ancestor hypothesized in the Paleogene) is a rule and not evidence for a causal link between conserved morphology, the neotenic development, and stable habitat. The D. aurora complex is one of the welldocumented examples of the long-term persistence of an ancient phenotype under very diverse and unstable environmental conditions.

Genetic Diversity in European Populations
The Palearctic populations of D. aurora do not represent a genetically homogenous group, and they split into the western and eastern clades in the five gene analyses ( Figure 1C). Additionally, two distinct cox1-5 haplotype groups are identified in Fennoscandia ( Figure 1B). As net-winged beetles depend heavily on rotten wood, we assume that the range of D. aurora was pushed to the south along with forest ecosystems during glacial maxima [103]. The high genetic diversity in the Fennoscandian population shows that the area only relatively recently reclaimed by forest habitats after the retreat of the continental ice sheet was populated from two long-term separated refugia, presumably located in southeastern Russia and western Europe. Similar diversification was identified in P. nigroruber ( Figure 1H, cox1-5 divergence 4.9% in Finland). Still, it stands at odds with the genetically homogenous Fennoscandian population of the other two North European net-winged beetles, P. minuta, and L. sanguineus ( Figure 1F,G). The dual colonization of Fennoscandia has been reported in some plants, mosses, and rodents [103][104][105]. Our findings open the questions of a possible reproductive isolation of sympatric Fennoscandian populations (mtDNA divergence up to 6.8%; Table S1) and the frequency with which various dispersal routes were employed during the recolonization of Northern Europe after the LGM and which species were able to retreat only to some refugia. Further research must be based on more extensive data than we currently have at our disposal. We need to densely sample the populations from the whole range including Eastern Europe and Siberia and to employ multiple nuclear markers to robustly recover the colonization routes after the retreat of the continental ice sheet.

Conclusions
The high genetic diversification within the traditionally delimited morphospecies D. aurora exemplifies unlinked genetic and morphological divergence. The Holarctic distribution of D. aurora has been unique as no other net-winged beetle species is simultaneously known from two zoogeographic regions [17]. Regardless of morphological similarity, we have to resurrect the name D. coccinata for Nearctic populations. As we do not have deep insight into the structure of the whole genome, we cannot robustly identify the processes that control morphological uniformity. Provisionally, we assume the role of the stabilizing selection in the Mullerian mimicry ring (besides the possible intrinsic morphological stasis). The broad ecological valence of D. aurora questions the recent hypothesis linking the stable environment and morphological uniformity of net-winged beetles. The present results show the potential of poorly dispersing beetles for studies on the origin of the large regional faunas and the distribution dynamics since the LGM. With growing information content, the barcode database (www.boldsystems.org, accessed on 1 June 2022) becomes a powerful tool for taxonomy. Yet, the barcodes provide only the first clue. Therefore, we separate as full species only Nearctic and Palearctic populations for which we have additional data. The robust reconstruction of dispersal routes and eventually delimitation of further intraspecific taxa will need nuclear markers and a dense, now unattainable, sampling from the whole range.

Data Availability Statement:
The data presented in this study are available in the article.


Protocol for the operation of a breathing and vaping biomimetic robot to delineate real-time inhaled particle profile of electronic cigarettes

Summary We recently developed a robotic human vaping mimetic real-time particle analyzer (HUMITIPAA) to evaluate the impact of change in chemical constituents and breathing profiles of electronic cigarettes (ECs) on potential pulmonary toxicity. Here, we describe the fabrication procedure of EC mouthpiece(s), establishment of sensor saturation curve, and preparation of e-liquid and vaping device(s) for testing. We further detail steps for HUMITIPAA preparation and connection setup, followed by data collection and processing. For complete details on the use and execution of this protocol, please refer to Kaiser et al. (2021).1

SUMMARY
We recently developed a robotic human vaping mimetic real-time particle analyzer (HUMITIPAA) to evaluate the impact of change in chemical constituents and breathing profiles of electronic cigarettes (ECs) on potential pulmonary toxicity. Here, we describe the fabrication procedure of EC mouthpiece(s), establishment of sensor saturation curve, and preparation of e-liquid and vaping device(s) for testing. We further detail steps for HUMITIPAA preparation and connection setup, followed by data collection and processing. For complete details on the use and execution of this protocol, please refer to Kaiser et al. (2021). 1

BEFORE YOU BEGIN Background
The popularity of electronic cigarettes (ECs) has risen considerably in recent years. A growing concern has been EC use particularly among the youth population, with a 2019 study showing that 27.5% of US high school students reported using an EC in the last 30 days. 2 Despite gaining popularity in part due to public perception of ECs posing minimal health risks, many EC liquids contain a wide variety of compounds that have not been well-characterized for their toxicity when aerosolized and inhaled. 3,4 With EC technologies developing seemingly faster than the research on their safety, the development of systems to analyze the potential toxicological effects of ECs and their chemical constituents is paramount. In fact, there is a pressing need for better characterization of aerosols from ECs as they emerge.
We recently reported development of a bioinspired vaping robotic system (Human Vaping Mimetic Real-Time Particle Analyzer [HUMITIPAA]) to characterize quantity and distribution pattern of particulate matters (PM) at four size fractions (300 nm-1 mm, 1 mm-2.5 mm, 2.5 mm-4 mm, and 4 mm-10 mm) in EC aerosols in real-time, while emulating pathophysiological breathing and vaping topography. 1 The protocol below describes specific steps for analysis of vitamin E acetate (VEA) added to an e-liquid base (50:50 solution of propylene glycol -vegetable glycerin [PG:VG] in this case). However, the HUMITIPAA can be applied to study the impact of a variety of EC additives. Higher particle quantities would imply increased total particle inhaled into the lungs and may serve as a surrogate for an internal channel between the inlet and outlet that has a laser directed through it. The light from this laser is scattered when a particle passes by it. A photosensitive electrical component is then used to detect this scattered light and output a signal that is eventually translated to a reading of the particle size. To better control the PM reading, prevent interference with ambient PM, and allow the sensor to interface with the system's closed pneumatics, the sensor is placed in an air-tight gasket assembly. The PM sensor interfaces with an Arduino MEGA 2560 microcontroller, and I 2 C (B) The Vaping Robot (exploded view) is used to activate the electronic cigarettes (ECs) for a programmable puff duration, drawing aerosols into a reservoir where they can be diluted and sampled. It utilizes an array of pinch valves to direct the fluidic connections between the EC, vacuum pump, aerosol reservoir, PM sensor, and Breathing-Emulator. The electronics contain an on-board power supply and a microcontroller that controls an 8-channel relay and communicates with other elements in the HUMITIPAA system. (C) The Dilution Robot is used to dilute the aerosols drawn from the Vaping Robot with filtered air. The Dilution Robot uses a syringe assembly affixed to a linear actuator to mix and withdraw a calculated volume of aerosols in the reservoir. Once the aerosols have been withdrawn into the syringe actuation system, a series of dilutions is performed in a sample syringe while the aerosol reservoir is simultaneously purged by a vacuum pump on either the Dilution Robot or Vaping Robot. (D) The Breathing-Emulator acts as the biomimetic ''lungs'' of the system, inhaling and exhaling air to imitate the flow dynamics of various human breathing patterns. The Breathing-Emulator is made of a rigid acrylic frame that holds a stepper motor and the barrels of two air-tight syringes. A center carriage holds the syringe plungers and is secured within the rigid frame so that is free to move in the direction of the syringe actuation. The center carriage moves via a lead screw and nut system. A lead screw is affixed to the stepper motor and the nut is secured to the center carriage, allowing the rotation of the stepper motor to be translated into linear movement of the center carriage and, thus, actuation of the syringes. Retraction of the syringes simulates inhalation while compression simulates exhalation. (E) The PM sensor provides real-time particle count and mass concentration readings for the four particle size fractions (300 nm-1 mm, 1 mm-2.5 mm, 2.5 mm-4 mm, and 4 mm-10 mm). The sensor is pneumatically connected between the aerosol reservoir (connected to the sensor inlet) and the Breathing-Emulator (connected to the sensor outlet), enclosed in a gasket assembly to direct the flow of aerosols across the sensor. Reproduced with permission from Kaiser et al. 1 communication is used to operate it in this protocol. The raw data generated using HUMITIPAA is processed using a custom-developed program run in MATLAB. More information on the functionality and firmware of these elements is detailed under materials and equipment.
The protocol discussed here specifically details the preparations required to run experiments, and the steps to connect and operate its elements, collect experimental data, and process and analyze this data. For more information regarding the design and construction of each element of the HUMITIPAA system, please refer to Kaiser et al. (2021). 1 We would like to also highlight the ability of our platform for integration with Lung-on-a-Chip systems to evaluation of biological impact of EC aerosols. [6][7][8][9][10][11] Preparations prior to operation Create PDMS gaskets for PM sensor and EC mouthpiece

Timing: 1 day
The PM sensor needs to be secured within a sealed gasket to contain and direct the flow of aerosols through it. A gasket also needs to be developed to create a seal around the EC mouthpiece so that a negative pressure can be applied to activate the EC once secured to the Vaping Robot.
1. 3D print and cure the gasket molds for the PM sensor and EC mouthpiece.
a. There will be a minimum of 5 pieces total: the two-part assembly for the front half of the sensor gasket, the two-part assembly for the back of the sensor gasket, and at least one mold for the EC mouthpiece.
Note: This was done using FormLabs' Form 3B+ SLA printer (model number: PKG-F3B-WSVC-MSP-BASIC) with their Black Photopolymer Resin (model number: RS-F2-GPBK-04). If using different types of sensors or ECs, the molds will need to be custom-designed in a 3D modeling software. SOLIDWORKS Standard 2020 was used to design the molds in this protocol.
Note: STEP and STL files are available upon request.
2. Create the PDMS gasket pieces. a. Prepare PDMS using a base elastomer to curing agent ratio of 5:1. b. Pour PDMS into molds and cure at 60 C for 3 h. 3. Laser cut and prepare the acrylic gasket clamps for the sensor assembly ( Figure 2). a. Use 3 mm thick acrylic for the front pieces that contain the inlet and outlet ports, and 6 mm thick clear acrylic for the back plate that contains the cable connection port. b. Use an M3 tap to thread the 4 screw holes on the back plate. ii. Insert the posts into the front mold and clamp the ends using the acrylic gasket clamps and 4 screws. c. Insert the cable and seal all openings using partially cured PDMS.
i. Seal the cable inlet, the edges where the front and the back gaskets join, and around the transfer tubing posts.
CRITICAL: When sealing the openings with PDMS, ensure it is viscous enough so that it will not leak into the sensor and damage it.
CRITICAL: Ensure all openings are fully sealed, as leaks in the gasket can greatly reduce the PM count reading.
d. Cure the full gasket assembly for an additional 2 h at 60 C.
CRITICAL: The maximum storage temperature for the Sensirion SPS30 PM sensor used in this experiment is 70 C. Ensure this temperature is not exceeded when curing the assembly. If using a different sensor, check its datasheet to verify it will not be damaged at the selected curing temperature.
Test PM sensor to find its saturation curve The PM sensors that are accessible for the purpose of this experiment have a PM count threshold that is much lower than the PM count of undiluted EC aerosols. Reducing the PM count to below this threshold would require significant dilution which can compromise the consistency of the data through variability in the dilution process as well as add additional time to the sampling series. To avoid this issue, the saturation curve for the PM sensor was determined by sampling and plotting data of various dilutions of EC aerosols ( Figure 3). This provides insight into the particle count ranges that the sensor can accurately capture and, thus, appropriate dilution parameters for the experiment. 5. Ensure the PM sensor is running and ready to collect data. a. Connect the corresponding pins of the sensor's cable to its 12-volt power supply and microcontroller. b. Open the microcontroller's communication port.
i. Open the Arduino integrated development environment (IDE) and upload the sensor firmware to the connected microcontroller. ii. Open the serial port and follow its automatically generated directions to begin logging the PM reading when ready. 6. Sample aerosols from the EC and dilute.
a. Place the EC into the inlet of its gasket assembly and connect a syringe to the outlet. b. Draw vapor from the EC by retracting the syringe.
Note: The ideal flow rate for drawing EC aerosols with a syringe will result in the EC activating and deactivating instantaneous with the starting and stopping of the syringe retraction. This value typically falls within the range of 15 mL/s to 40 mL/s. If the flow rate is too slow, the EC will not activate; if the flow rate is too fast, there will be an inconsistent delay between the syringe retraction and the EC activation. Additionally, a minimum volume of 15 mL is recommended for the initial draw of vapor.
c. Disconnect the syringe from the EC gasket assembly, i. Attach a syringe filter (0.2 mm or finer) to the output.
ii. Dilute the EC aerosols with filtered air.
Note: For the saturation curve shown in Figure 3, 15 mL of vapor was drawn from the EC then depressed to 6 mL. The syringe was then retracted to 60 mL with filtered air to achieve a dilution factor of 10. Each repetition of this exponentially increased this dilution factor.
CRITICAL: To reduce the margin of error, dilutions should be done using a minimum volume that is no less than 10% of the total syringe volume. For example, a dilution factor of 10 could be achieved by mixing 5 mL of vapor with 45 mL of filtered air. However, a dilution factor of 100 should not be achieved by mixing 0.5 mL of vapor with 49.5 mL of filtered air, and instead be done using two iterations of the 10-fold dilution.
7. Record the PM count of the diluted aerosols. a. Attach a syringe filter to the PM sensor outlet. b. Using an 18-gauge blunt needle with the syringe, insert the needle into the transfer tubing for the PM sensor inlet and inject the aerosols directly into the PM sensor using a consistent flow rate that approximately matches that of the Breathing-Emulator.
Note: Since the volume displacement of the Breathing-Emulator syringes ranges from 23 mL to 38 mL depending on the selected breathing profile, the recommended minimum final volume of diluted aerosols to pass through the sensor is 30 mL.
c. After injecting the aerosols, replace the syringe filter onto the syringe outlet, and continue injecting filtered air into the sensor until the PM reading returns to approximately 0 #/cm 3 . 8. Repeat steps 6 and 7 at least 3 times for each dilution factor, then repeat for various dilution factors until a clear saturation curve can be seen. a. Once the desired number of repetitions for one dilution factor is completed, stop the PM sensor reading using the serial port. i. Save the serial port reading for each dilution factor by copying it to a .txt file.

OPEN ACCESS
Note: Processing the data requires the total readings for each dilution factor to be saved separately. For this experiment, there were 11 total .txt files, with each file containing the data for 4 separate aerosol samples.
9. Process the collected data for visualization and analysis. a. Refer to ''process raw data'' in the step-by-step method details for an explanation of the data processing software. b. Run the ''Dilution Validation'' MATLAB script.
Note: This script will output two Excel files that summarize the peak particle count and concentration values for each sample (''ParticleDilutionDataCountTable'' and ''ParticleDilution-DataConcTable''). This data can be transferred to GraphPad Prism for further processing.
Prepare the e-liquid mixtures

Timing: 1 h
Note: This outlines the process for preparing the 50:50 PG:VG base e-liquid and solutions of 1.25%, 2.5%, and 5% (v/v) VEA concentrations. However, VEA may be substituted with other additives, as long as the appropriate measures are taken to thoroughly incorporate the additives into the mixture. 10. Heat the glycerol (VG) to reduce its viscosity and allow for easier measuring and mixing.
a. For this experiment, this was done by placing the glycerol in the Inhalation Exposure Chamber, heated to 37 C, for 15 min. 11. Measure out equal parts by volume of glycerol and propylene glycol (PG) into a mixing container.
Note: For this experiment, 25 mL of each component were measured using a serological pipette.
Optional: Alternatively, these components may be measured out by weight but the different densities (1.036 g/cm 3 for PG and 1.25 g/cm 3 for VG) must be considered.
12. Place the container into a mixer and mix for 5 min, followed by 2 min of deaeration. A non-vacuum planetary centrifugal mixer (Thinky, model ARE-310) was used for this experiment.
CRITICAL: Ensure the mixture is properly deaerated prior to measuring it out for the VEA solutions. If using another method to mix the solution, heating the solution may help with deaeration as the air bubbles will dissipate quicker.
13. Measure out 19 mL of the base e-liquid to a separate container and add 1.0 mL of VEA to create a concentration of 5%. 14. Thoroughly  The e-liquid solution should be stored protected from light and between 2 C and 8 C, with a maximum storage time of four months.

OPEN ACCESS
executed can be programmatically varied. While the vapor is diluting in the Dilution Robot, the Vaping Robot receives a signal from the Dilution Robot to purge the reservoir. The pneumatic layout of the Vaping Robot is detailed in Figure 4. The Dilution Robot's Arduino code calculates the system's dilution parameters given a userdefined dilution factor, and controls all electrical components (pinch valves, vacuum pump, and the linear actuator that controls the syringe assembly). Similar to the Vaping Robot, the Dilution Robot uses a relay that interfaces with an Arduino UNO. The layout of the Dilution Robot is shown in Figure 5. Once vapor is drawn into the reservoir by the Vaping Robot, it is mixed between the syringes (sample syringe empties its contents, mixing syringe is filled). The sample syringe then fills with the diluted vapor for further dilution (through the filters at pinch valve 1). While the vapor is diluting in the sample syringe, the reservoir and tubing dead volume are being purged. This is done using the purge states of both the Dilution Robot and Vaping Robot. The diluted vapor is then mixed back into the reservoir to be sampled using the Breathing-Emulator. This code also has a ''tube cleaning'' parameter. This uses the syringes and vacuum to push air through the tubing and connects to the Vaping Robot which can be manually controlled to clear its tubing of residual EC condensate and/or suspended vapor.  The Breathing-Emulator's Arduino code controls the element's stepper motor, which drives the syringes' volume displacement; it also outputs a signal to the Vaping Robot on each inhale, triggering the Vaping Robot to step through its states. The breathing profile is set in this firmware (normal, obstructive, or restrictive), and must match the breathing profile set in the Vaping Robot firmware, mainly for timing constraints. It also has two end-stops: one to trigger the Breathing-Emulator to begin once the syringes are fully compressed, and the other to reset the Breathing-Emulator if the stepper motor stalls and the center carriage drifts. The custom-designed data parsing software converts the text files of the PM sensor's raw data into Excel tables, separating the particle count and mass concentration. Tabulating the data allows for further processing in MATLAB. The MATLAB script is designed to search through the tabulated data files and find the location of the peaks (where the aerosols are sampled). It then gathers 6 data points spanning each peak to show the real-time particle count as they are being inhaled and exhaled. This data span begins when the PM count reaches 80% of the peak's PM count as shown in Figure 6.

STEP-BY-STEP METHOD DETAILS
Prepare and connect HUMITIPAA components Build-up of e-liquid in the tubing can obstruct the pneumatics of the system and ultimately affect the PM reading. Failure to properly connect the components will result in poor data collection and interruptions to the experiment. This step ensures that all systems are clean and functioning properly with each other. This will provide more accurate results and prevent interruptions.
1. Prepare the Inhalation Exposure Chamber. a. Open the Inhalation Exposure Chamber's code in the Arduino IDE. b. Set the temperature, humidity, and CO 2 parameters in the code and upload it to the chamber's microcontroller. i. Set the temperature to 37 C, humidity to 70% (RH%), and CO 2 levels to 5%.
Note: It may take up to 3 h for the Inhalation Exposure Chamber to equilibrate upon start up. Upon reaching this point, the temperature, humidity, and CO 2 levels significantly drop when the chamber is opened. Allow a minimum of 20 min for the chamber to equilibrate after it has been opened.
2. If the system was used previously, replace all pneumatic components of the setup that come into contact with EC aerosols. Figure 6. Graph of PM 300 nm-1 mm data points collected over one peak and the criteria for determining which points to select and tabulate Visual of the MATLAB data processing script's criteria for selecting data points to span an aerosol sample over one breathing interval. Graph shows example data collected over one peak at a sampling rate of approximately 1 Hz. Six points are selected sequentially, beginning with the first data point that passes counting threshold. CRITICAL: Always make sure systems are powered off and unplugged before handling beyond normal experimental operation. Figure 8). i. Connect the PM sensor ( Figure 9A) and Breathing-Emulator ( Figure 9B) to their corresponding microcontrollers. b. Connect pneumatics from the Vaping Robot to the PM sensor and Breathing-Emulator and insert the EC in the system ( Figure 10).

Place and connect robots inside the Inhalation Exposure Chamber. a. Connect communication lines between the robots' microcontrollers (
Note: The image of the full setup in the Inhalation Exposure Chamber is shown in Figure 11.
4. Define the system parameters for the PM sensor, Breathing-Emulator, Dilution Robot, and Vaping Robot. a. Define the sampling rate in the PM sensor firmware and test. i. Run the sensor for 5 min to ensure that the sampling interval is not too short.
Note: The sampling rate was set to was set to 1 Hz for this experiment. This is the sensor's limit, so setting this value any higher may result in a poor reading. When the sampling rate is set too fast, an overflow (''ovf'') warning will appear in the mass concentration reading. Note: This file's code allows the user to manually input the retraction length then see how it translates to the syringe assembly. The minimum and maximum actuator limits are the pulse values for which the sample syringe is retracted to 0 mL and 30 mL.
d. Select breathing profile in the Breathing-Emulator software, ensuring it matches the profile selected in the Vaping Robot's firmware. 5. Verify that the parameters and system setup provide an acceptable PM reading using a control 50:50 PG:VG mixture. Refer to the ''collect data'' step detailed below for the process for running the HUMITIPAA system to collect this PM data.
CRITICAL: Between samples, the PM sensor particle count and mass concentration readings should drop to 0 #/cm 3 and 0 mg/m 3 . Since the pneumatics of the HUMITIPAA system are contained with all access to air being filtered, the PM reading should not be influenced by ambient PM. (Troubleshooting 1).
a. Ensure that the PM reading during the sampling of aerosols is sufficiently below the sensor's saturation threshold that was previously determined. If not, adjust the dilution ratio accordingly (troubleshooting 2).
Note: For this protocol, a dilution factor of 10,000 was used to collect the 300 nm-1 mm data, and a dilution factor of 900 was used to collect the 1 mm-10 mm data.
Note: Testing the dilution parameters using a consistent control PG:VG mixture prior to each experiment validates the Dilution Robot's efficacy and accuracy over time.

Figure 8. Wiring diagram for communication between microcontrollers
The microcontrollers communicate using binary (high/low) signals. All electrical grounds must be connected within the system to ensure proper communication between microcontrollers (blue lines). The Breathing-Emulator signals the Vaping Robot to step through its states (green line). The Vaping Robot triggers the Dilution Robot to either dilute the EC aerosols or to clear the system of all aerosols. The Dilution Robot sends a signal to the Vaping Robot to utilize its aerosol purging function (yellow lines).

Timing: 4 days
This step outlines the process for collecting data to ensure consistency from the system and prevent cross-contamination between trials.
6. Load the Arduino firmware for each robot and monitor the serial ports. a. Open each Arduino IDE with code associated with each robot. b. Open the serial ports so that the data on each serial port can be monitored or recorded simultaneously. 7. The Breathing-Emulator drives the rest of the setup, power this on to begin the experiment.
a. Leave setup running until the desired number of puffs is recorded (troubleshooting 3). 8. After the data is recorded, stop the setup, pause the PM sensor, and charge the EC. 9. Copy the PM sensor serial monitor data into a .txt file. For easier data processing, save with a descriptive file name, followed by the trial number formatted as ''T#'', and ensure all subsequent files follow the same format (e.g., ''0_Percent_VEA_T1.txt'', ''1.25_Percent_VEA_T1.txt'' .) (troubleshooting 4).
CRITICAL: As the dilution factor increases, the disparity between the submicron particle count (300 nm-1 mm) and the microparticle counts (1 mm-10 mm) greatly increases, resulting in low microparticle counts. This can result in high variability in the microparticle measurements, while the submicron particle count is fairly consistent. This means that two trials at separate dilutions will likely be required to record accurate data for all particle size ranges.
10. Run the tube cleaning cycle for the Dilution Robot and Vaping Robot twice. a. Set the ''tube cleaning'' parameter to 1 in the Dilution Robot code's run settings, as shown in the following snippet of code: is no visible e-liquid passing through the transfer tubing. 11. Replace the EC with the next concentration to be studied and repeat experiment until the data for all desired concentrations has been collected. 12. After all data has been collected, examine it to ensure there are no inconsistencies/missed puffs.
If there are, repeat the data collection for that concentration before cleaning the system.
CRITICAL: Replacing the pneumatic components can slightly affect the PM reading. So, it is imperative that all concentrations are studied within the same trial, before replacing any components. When replacing tubing, make a best effort to align the tubing in the same fashion as it was previously.
13. Once the trial is complete, remove the setup from the Inhalation Exposure Chamber for cleaning. a. Clear all pneumatic connectors and tubing of excess e-liquid or replace with new components.
Note: To avoid the introduction of foreign materials to the system, the tubing and connectors can be cleared of e-liquid accumulation using filtered compressed air or a filtered vacuum. If these components dirty from e-liquid build-up over multiple uses, they must be replaced.
Note: Due to accumulation of the e-liquid in the tubing and connectors, it is not advised to run more than 50 puffs without replacing the pneumatic connectors. So, for an experiment measuring 4 concentrations, it is advised to have 9 puffs per concentration (to give room to repeat a concentration if needed), then manually replace and clean the pneumatic components between trials.
14. Fully charge the EC battery between trials. 15. Reassemble the setup in the Inhalation Exposure Chamber. 16. Wait for the chamber to equilibrate. 17. Repeat this data collection process for as many trials as desired.
>#define DEBUG 1 Figure 10. Image of Vaping Robot's pneumatic connections to other elements of the system and electronic cigarette placed in gasket assembly Pneumatic ports in the Vaping Robot are used to connect to the Breathing-Emulator and PM sensor inlet and outlet. The EC gasket assembly is connected to the Vaping Robot's pneumatic assembly to draw aerosols from the EC.
Optional: If the microparticle counts within the 1 mm-10 mm size fractions are too low, repeat the experiment using a lower dilution factor. Note that this will likely mean the submicron particle count (300 nm-1 mm) will be beyond the sensor's linearity region, so the data for these particle size ranges would need to be processed separately.

Process raw data
Timing: 10 min (for step 18) This step converts the raw data into table form for further processing and analysis. It also shows a graph of the collected data over time to verify that there were no immediate issues with the experiment.
Note: This software will output two Excel files for each raw text file. The Excel filenames will be that of the text file followed by 'N' for the particle count data, and 'M' for the mass concentration data. It will also output a ''ProcessedFiles'' text file that lists the names of these Excel files. This text file is necessary to run the MATLAB code in the next step. c. Run the program to process the data.

Note:
The processed data will be saved into Excel files named ''SpanDataCount'' for the PM count data and ''SpanDataConc'' for the PM concentration data. Graphs of the real-time collected data will appear for each file (Figure 12).
d. Review the tables of processed data ( Table 2) and save under a different file name, or in a separate location, as the Excel files will be written over every time the MATLAB script is run.
CRITICAL: The data is tabulated based on the order that the files are processed. If the order of the file names in ProcessedFiles.txt is incorrect, the trial numbers in SpanData-Count.xlsx will be mislabeled.
20. Repeat this process for each concentration.

EXPECTED OUTCOMES
For a 50:50 PG:VG mixture with no additives at a dilution factor of 10,000, we get a submicron particle count (300 nm-1 mm) that generally peaks between 15,000 #/cm 3 and 30,000 #/cm 3 . At a dilution factor of 900, we get a microparticle count that peaks between 2,000 #/cm 3 and 6,000 #/cm3 for the 1 mm-2.5 mm size fraction, 350 #/cm 3 and 800 #/cm 3 for the 2.5 mm-4 mm fraction, and 40 #/cm 3 and 120 #/cm 3 for the 4 mm-10 mm fraction. These counts are expected to increase with the VEA additive.

QUANTIFICATION AND STATISTICAL ANALYSIS
The peak span data output from the MATLAB script is processed in GraphPad Prism. It is first tested for normal distribution among the data sets using a Shapiro-Wilk test (a=0.05). If the distributions of The custom-designed data processing software outputs separate Excel files tabulating the particle count and mass concentration data for each raw data file directly copied from the PM sensor readings. An additional text file lists all files processed in the software. These files are manually copied into the MATLAB program's path for further processing and analysis.

LIMITATIONS
Most particulate sensors that can operate with this system have a relatively low saturation point. This means the system requires significant diluting of the vapor to make sure that the sensor is operating within its linearity region. If the values increase as the study continues such that the count begins falling outside of the linearity region, the results cannot be trusted and the experimental setup would need to be restructured to compensate for this.

TROUBLESHOOTING
Problem 1 PM count not returning to zero between samples (step 5).

Potential solution
Check that there are no leaks in the tubing, connectors, or PM sensor gasket assembly that may be introducing unfiltered air into the system. This may also result from the vacuum pump not sufficiently purging the system of EC aerosols between samples. This can occur when there are leaks or obstructions in the system. It can also occur if the vacuum pump or its filter need to be replaced. The pump can be tested individually with a pressure gauge to ensure it is creating a substantial negative pressure (-60 kPa or less).

Potential solution
First, check that there are no leaks or obstructions in the pneumatics and that all pinch valves are working properly. Other than this, this issue may be due to the sample syringe not fully emptying prior to intaking the aerosols for dilution or mixing, or not completely filling with aerosols during The MATLAB script tabulates that data such that the first column labels which peak the data points belong to, and the first and second rows label the trial number and particle fraction of the data below it. This table shows data collected for the submicron particle fraction.
these processes. For this, the speed of the linear actuator can be increased on its control board, or the time delay set for the actuation may need to be increased in the Dilution Robot firmware. Also, check the alignment of the syringe actuation assembly, as misalignment can introduce resistance to the actuator movement and slow it down.

Problem 3
HUMITIPAA components desynchronizing when running the system.

Potential solution
First, ensure that all electrical connections are secured between the microcontrollers and that they share a common electrical ground. Loose or missing connections will result in the components receiving or sending faulty signals, causing timing issues or skipped steps by the components. After this, if the issue is occurring in the Dilution Robot specifically (i.e., the dilution cycle does not finish before sampling), the issue can be resolved in the firmware. The time delay set for the linear actuator in the Dilution Robot code can be decreased if the linear actuator speed is sufficient to compensate for it. If not, the Vaping Robot firmware can be modified by adding more states between the EC activation and aerosol sampling states to give additional time for the dilution cycle to finish.

Problem 4
Unexpectedly high or low PM readings (step 7).

Potential solution
Check that there are no clogs in the tubing or connectors. Airflow obstruction and/or leaks in any path will affect the reading. If the reading is too low, there may be an obstruction and/or leak in the path to inhale the vapor, the path to the sensor, or the path from the mixing syringe. If the reading is too high, the vapor may not be diluting properly or the reservoir may not be fully evacuating during dilution. This can be due to an airflow obstruction along the Vaping Robot's purge path or the Dilution Robot's evacuation path, it can also be due to mechanical issues with the actuation of syringes. Also verify that the pinch valves are all working by manually checking each one.

Problem 5
MATLAB script is not running (step 19).

Potential solution
The error message often explains the issue but is sometimes unclear. One common cause for this is the sampling rate for the PM sensor being set too high. If an overflow warning occurs, it will not be caught in the data parsing software and will be recorded as ''ovf'' when tabulated. The MATLAB code will not be able to read this and give a warning saying that the matrix dimensions of the files Figure 12. MATLAB graph of all PM data for nine puffs of the control 50:50 PG:VG e-liquid Plot that is automatically generated by the MATLAB script showing the particle count data with respect to time, where PM1 labels the particles from 300 nm-1 mm, PM2.5 from 1 mm-2.5 mm, PM4 from 2.5 mm-4 mm, and PM10 from 4 mm-10 mm. Note that the readings for PM2.5 -PM10 may not be visible due to the large difference relative to PM1 and scaling. The vertical lines are located at the maximum values for each peak.

OPEN ACCESS
do not agree. Another cause for this warning is if the PM recording was interrupted before completing a sample. This will result in the mass concentration reading not being fully documented after the particle count reading and, therefore, the length of the two readings not matching.

RESOURCE AVAILABILITY
Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Kambez H. Benam (benamk@pitt.edu).

Materials availability
This study did not generate new unique reagents.

Data and code availability
The data that support the findings of this study, the code for analysis and any additional information are available from the corresponding author upon reasonable request.